COSC 3337 - Practice 9 - Support Vector Machine - Ujwal Joshi

About The Data

We'll be using the Breast Cancer Wisconsin (Diagnostic) Data Set from kaggle for this lab, but feel free to follow along with your own dataset. The dataset contains a total of 32 columns, with following attribute information:

1) ID number

2) Diagnosis (M = malignant, B = benign)

3-32)

Ten real-valued features are computed for each cell nucleus:

• a) radius (mean of distances from center to points on the perimeter)
• b) texture (standard deviation of gray-scale values)
• c) perimeter
• d) area
• e) smoothness (local variation in radius lengths)
• f) compactness (perimeter^2 / area - 1.0)
• g) concavity (severity of concave portions of the contour)
• h) concave points (number of concave portions of the contour)
• i) symmetry
• j) fractal dimension ("coastline approximation" - 1)

The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.

Our goal will be to predict the diagnosis (benign or malignant).

Exploratory Data Analysis

Let's begin by importing some necessary libraries that we'll be using to explore the data.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

from matplotlib import rcParams
rcParams['figure.figsize'] = 15, 5
sns.set_style('darkgrid')

Our first step is to load the data into a pandas DataFrame.

breast_cancer_df = pd.read_csv('data.csv')
breast_cancer_df.head()

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	...	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890	NaN
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	...	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902	NaN
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	...	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758	NaN
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	...	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300	NaN
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	...	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678	NaN

5 rows × 33 columns

# Modified Work
breast_cancer_df.tail()

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
564	926424	M	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	...	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115	NaN
565	926682	M	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	...	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637	NaN
566	926954	M	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	...	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820	NaN
567	927241	M	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	...	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400	NaN
568	92751	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	...	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039	NaN

5 rows × 33 columns

There's an odd column "Unnamed: 32", which we'll go ahead and drop since it's full of NaN values. We also won't need the id label, so we can drop that as well.

breast_cancer_df.drop(labels=['Unnamed: 32', 'id'], axis=1, inplace=True)

Since a lot of the features in this dataset can be hard to interpret without domain knowledge of cancer or tumor cells, we'll just do a few visualizations here, but feel free to explore as much as you'd like before constructing a model.

breast_cancer_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 31 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   diagnosis                569 non-null    object 
 1   radius_mean              569 non-null    float64
 2   texture_mean             569 non-null    float64
 3   perimeter_mean           569 non-null    float64
 4   area_mean                569 non-null    float64
 5   smoothness_mean          569 non-null    float64
 6   compactness_mean         569 non-null    float64
 7   concavity_mean           569 non-null    float64
 8   concave points_mean      569 non-null    float64
 9   symmetry_mean            569 non-null    float64
 10  fractal_dimension_mean   569 non-null    float64
 11  radius_se                569 non-null    float64
 12  texture_se               569 non-null    float64
 13  perimeter_se             569 non-null    float64
 14  area_se                  569 non-null    float64
 15  smoothness_se            569 non-null    float64
 16  compactness_se           569 non-null    float64
 17  concavity_se             569 non-null    float64
 18  concave points_se        569 non-null    float64
 19  symmetry_se              569 non-null    float64
 20  fractal_dimension_se     569 non-null    float64
 21  radius_worst             569 non-null    float64
 22  texture_worst            569 non-null    float64
 23  perimeter_worst          569 non-null    float64
 24  area_worst               569 non-null    float64
 25  smoothness_worst         569 non-null    float64
 26  compactness_worst        569 non-null    float64
 27  concavity_worst          569 non-null    float64
 28  concave points_worst     569 non-null    float64
 29  symmetry_worst           569 non-null    float64
 30  fractal_dimension_worst  569 non-null    float64
dtypes: float64(30), object(1)
memory usage: 137.9+ KB

calling .info() we see that there are no missing values in this dataset.

# Modified Work
breast_cancer_df.describe()

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
count	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	...	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000
mean	14.127292	19.289649	91.969033	654.889104	0.096360	0.104341	0.088799	0.048919	0.181162	0.062798	...	16.269190	25.677223	107.261213	880.583128	0.132369	0.254265	0.272188	0.114606	0.290076	0.083946
std	3.524049	4.301036	24.298981	351.914129	0.014064	0.052813	0.079720	0.038803	0.027414	0.007060	...	4.833242	6.146258	33.602542	569.356993	0.022832	0.157336	0.208624	0.065732	0.061867	0.018061
min	6.981000	9.710000	43.790000	143.500000	0.052630	0.019380	0.000000	0.000000	0.106000	0.049960	...	7.930000	12.020000	50.410000	185.200000	0.071170	0.027290	0.000000	0.000000	0.156500	0.055040
25%	11.700000	16.170000	75.170000	420.300000	0.086370	0.064920	0.029560	0.020310	0.161900	0.057700	...	13.010000	21.080000	84.110000	515.300000	0.116600	0.147200	0.114500	0.064930	0.250400	0.071460
50%	13.370000	18.840000	86.240000	551.100000	0.095870	0.092630	0.061540	0.033500	0.179200	0.061540	...	14.970000	25.410000	97.660000	686.500000	0.131300	0.211900	0.226700	0.099930	0.282200	0.080040
75%	15.780000	21.800000	104.100000	782.700000	0.105300	0.130400	0.130700	0.074000	0.195700	0.066120	...	18.790000	29.720000	125.400000	1084.000000	0.146000	0.339100	0.382900	0.161400	0.317900	0.092080
max	28.110000	39.280000	188.500000	2501.000000	0.163400	0.345400	0.426800	0.201200	0.304000	0.097440	...	36.040000	49.540000	251.200000	4254.000000	0.222600	1.058000	1.252000	0.291000	0.663800	0.207500

8 rows × 30 columns

# Modified Work
unique_vals_breast_cancer_df = pd.DataFrame({
    'Unique Value Count': breast_cancer_df.apply(lambda col: len(col.unique())),
    'Unique Values': breast_cancer_df.apply(lambda col: col.unique())
})

unique_vals_breast_cancer_df

	Unique Value Count	Unique Values
diagnosis	2	[M, B]
radius_mean	456	[17.99, 20.57, 19.69, 11.42, 20.29, 12.45, 18....
texture_mean	479	[10.38, 17.77, 21.25, 20.38, 14.34, 15.7, 19.9...
perimeter_mean	522	[122.8, 132.9, 130.0, 77.58, 135.1, 82.57, 119...
area_mean	539	[1001.0, 1326.0, 1203.0, 386.1, 1297.0, 477.1,...
smoothness_mean	474	[0.1184, 0.08474, 0.1096, 0.1425, 0.1003, 0.12...
compactness_mean	537	[0.2776, 0.07864, 0.1599, 0.2839, 0.1328, 0.17...
concavity_mean	537	[0.3001, 0.0869, 0.1974, 0.2414, 0.198, 0.1578...
concave points_mean	542	[0.1471, 0.07017, 0.1279, 0.1052, 0.1043, 0.08...
symmetry_mean	432	[0.2419, 0.1812, 0.2069, 0.2597, 0.1809, 0.208...
fractal_dimension_mean	499	[0.07871, 0.05667, 0.05999, 0.09744, 0.05883, ...
radius_se	540	[1.095, 0.5435, 0.7456, 0.4956, 0.7572, 0.3345...
texture_se	519	[0.9053, 0.7339, 0.7869, 1.156, 0.7813, 0.8902...
perimeter_se	533	[8.589, 3.398, 4.585, 3.445, 5.438, 2.217, 3.1...
area_se	528	[153.4, 74.08, 94.03, 27.23, 94.44, 27.19, 53....
smoothness_se	547	[0.006399, 0.005225, 0.00615, 0.00911, 0.01149...
compactness_se	541	[0.04904, 0.01308, 0.04006, 0.07458, 0.02461, ...
concavity_se	533	[0.05373, 0.0186, 0.03832, 0.05661, 0.05688, 0...
concave points_se	507	[0.01587, 0.0134, 0.02058, 0.01867, 0.01885, 0...
symmetry_se	498	[0.03003, 0.01389, 0.0225, 0.05963, 0.01756, 0...
fractal_dimension_se	545	[0.006193, 0.003532, 0.004571, 0.009208, 0.005...
radius_worst	457	[25.38, 24.99, 23.57, 14.91, 22.54, 15.47, 22....
texture_worst	511	[17.33, 23.41, 25.53, 26.5, 16.67, 23.75, 27.6...
perimeter_worst	514	[184.6, 158.8, 152.5, 98.87, 152.2, 103.4, 153...
area_worst	544	[2019.0, 1956.0, 1709.0, 567.7, 1575.0, 741.6,...
smoothness_worst	411	[0.1622, 0.1238, 0.1444, 0.2098, 0.1374, 0.179...
compactness_worst	529	[0.6656, 0.1866, 0.4245, 0.8663, 0.205, 0.5249...
concavity_worst	539	[0.7119, 0.2416, 0.4504, 0.6869, 0.4, 0.5355, ...
concave points_worst	492	[0.2654, 0.186, 0.243, 0.2575, 0.1625, 0.1741,...
symmetry_worst	500	[0.4601, 0.275, 0.3613, 0.6638, 0.2364, 0.3985...
fractal_dimension_worst	535	[0.1189, 0.08902, 0.08758, 0.173, 0.07678, 0.1...

sns.pairplot(breast_cancer_df, hue='diagnosis', vars=['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 
                                     'smoothness_mean', 'compactness_mean', 'concavity_mean',
                                     'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean'])
plt.show()

c:\Users\Ujwal\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

# Modified Work
sns.pairplot(breast_cancer_df, hue='diagnosis', vars=['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 
                                     'smoothness_mean', 'compactness_mean', 'concavity_mean',
                                     'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean'], corner=True)
plt.show()

c:\Users\Ujwal\anaconda3\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

There seems to be pretty good distinction between the diagnosis (blue & orange) in most of the atributes above.

sns.countplot(x=breast_cancer_df['diagnosis'])
plt.show()

Majority of our data observations are of the benign class.

sns.scatterplot(x = 'area_mean', y = 'smoothness_mean', hue = 'diagnosis', data = breast_cancer_df)
plt.show()

area_mean could be a good predictor wheather malignant or benign since there is pretty good separation here. Most benign (orange) have area_mean of around 500 or lower.

# Modified Work
sns.boxenplot(breast_cancer_df, x='area_mean')
plt.show()

plt.figure(figsize=(20,10)) 
sns.heatmap(breast_cancer_df.drop(columns=['diagnosis']).corr(), annot=True) 
plt.show()

Some strong correlations are present. (very bright squares for example)

# Modified Work
plt.figure(figsize=(20,10)) 
sns.heatmap(breast_cancer_df.drop(columns=['diagnosis']).corr(), annot=True, linewidth=.5, cmap="crest") 
plt.show()

Pre-Processing

Let's go ahead and scale our data before training and creating our model

from sklearn.preprocessing import StandardScaler

# all columns except 'Outcome'
X = breast_cancer_df.drop('diagnosis', axis=1)
y = breast_cancer_df['diagnosis']

# create our scaler object
scaler = StandardScaler()
# use our scaler object to transform/scale our data and save it into X_scaled
X_scaled = scaler.fit_transform(X)
# reassign X to a new DataFrame using the X_scaled values.
X = pd.DataFrame(data=X_scaled, columns=X.columns)

X.head()

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	1.097064	-2.073335	1.269934	0.984375	1.568466	3.283515	2.652874	2.532475	2.217515	2.255747	...	1.886690	-1.359293	2.303601	2.001237	1.307686	2.616665	2.109526	2.296076	2.750622	1.937015
1	1.829821	-0.353632	1.685955	1.908708	-0.826962	-0.487072	-0.023846	0.548144	0.001392	-0.868652	...	1.805927	-0.369203	1.535126	1.890489	-0.375612	-0.430444	-0.146749	1.087084	-0.243890	0.281190
2	1.579888	0.456187	1.566503	1.558884	0.942210	1.052926	1.363478	2.037231	0.939685	-0.398008	...	1.511870	-0.023974	1.347475	1.456285	0.527407	1.082932	0.854974	1.955000	1.152255	0.201391
3	-0.768909	0.253732	-0.592687	-0.764464	3.283553	3.402909	1.915897	1.451707	2.867383	4.910919	...	-0.281464	0.133984	-0.249939	-0.550021	3.394275	3.893397	1.989588	2.175786	6.046041	4.935010
4	1.750297	-1.151816	1.776573	1.826229	0.280372	0.539340	1.371011	1.428493	-0.009560	-0.562450	...	1.298575	-1.466770	1.338539	1.220724	0.220556	-0.313395	0.613179	0.729259	-0.868353	-0.397100

5 rows × 30 columns

# Modified Work
y.head()

0    M
1    M
2    M
3    M
4    M
Name: diagnosis, dtype: object

Creating our Model

We're now ready to begin creating and training our model. We first need to split our data into training and testing sets. This can be done using sklearn's train_test_split(X, y, test_size) function. This function takes in your features (X), the target variable (y), and the test_size you'd like (Generally a test size of around 0.3 is good enough). It will then return a tuple of X_train, X_test, y_train, y_test sets for us. We will train our model on the training set and then use the test set to evaluate the model.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)

# Modified Work
X2_train, X2_test, y2_train, y2_test = train_test_split(X, y, test_size=0.2, random_state=101)

from sklearn.svm import SVC

# instantiate the model with default parameters
model = SVC()
# fit/train 
model.fit(X_train,y_train)

SVC()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# Modified Work
model2 = SVC()
model2.fit(X2_train, y2_train)

SVC()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Model Evaluation

Now that we've finished training, we can make predictions off of the test data and evaluate our model's performance using the corresponding test data.

predictions = model.predict(X_test)

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test, predictions)

print('Confusion matrix\n\n', cm)
print('\nTrue Positives(TP) = ', cm[0,0])
print('\nTrue Negatives(TN) = ', cm[1,1])
print('\nFalse Positives(FP) = ', cm[0,1])
print('\nFalse Negatives(FN) = ', cm[1,0])

Confusion matrix

 [[104   1]
 [  3  63]]

True Positives(TP) =  104

True Negatives(TN) =  63

False Positives(FP) =  1

False Negatives(FN) =  3

from sklearn.metrics import classification_report

print(classification_report(y_test,predictions))

              precision    recall  f1-score   support

           B       0.97      0.99      0.98       105
           M       0.98      0.95      0.97        66

    accuracy                           0.98       171
   macro avg       0.98      0.97      0.98       171
weighted avg       0.98      0.98      0.98       171

# Modified Work
predictions2 = model2.predict(X2_test)

cm2 = confusion_matrix(y2_test, predictions2)

print('Confusion matrix\n\n', cm2)
print('\nTrue Positives(TP) = ', cm2[0,0])
print('\nTrue Negatives(TN) = ', cm2[1,1])
print('\nFalse Positives(FP) = ', cm2[0,1])
print('\nFalse Negatives(FN) = ', cm2[1,0])

Confusion matrix

 [[71  1]
 [ 1 41]]

True Positives(TP) =  71

True Negatives(TN) =  41

False Positives(FP) =  1

False Negatives(FN) =  1

print(classification_report(y2_test, predictions2))

              precision    recall  f1-score   support

           B       0.99      0.99      0.99        72
           M       0.98      0.98      0.98        42

    accuracy                           0.98       114
   macro avg       0.98      0.98      0.98       114
weighted avg       0.98      0.98      0.98       114

Hyperparameter Tuning

Finding the right parameters (like what C or gamma values to use) is a tricky task, but luckily we can be a little lazy and just try a bunch of combinations and see what works best. This idea of creating a 'grid' of parameters and just trying out all the possible combinations is called a Gridsearch, this method is common enough that Scikit-learn has this functionality built in with GridSearchCV. The CV stands for cross-validation.

GridSearchCV takes a dictionary that describes the parameters that should be tried and a model to train. The grid of parameters is defined as a dictionary, where the keys are the parameters and the values are the settings to be tested. Let's go ahead and try a few different parameters to see which of them is the best set to use.

param_grid = {'C': [0.1,1, 10, 100, 1000], 'gamma': [1,0.1,0.01,0.001,0.0001], 'kernel': ['rbf']} 

You should add refit=True and choose verbose to whatever number you want. The higher the number, the more verbose.(verbose just means the text output describing the process).

from sklearn.model_selection import GridSearchCV

grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=3)

What fit does is a bit more involved than usual. First, it runs the same loop with cross-validation to find the best parameter combination. Once it has the best combination, it runs fit again on all data passed to fit (without cross-validation), to build a single new model using the best parameter setting.

grid.fit(X_train,y_train)

Fitting 5 folds for each of 25 candidates, totalling 125 fits
[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.625 total time=   0.0s
[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s
[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s
[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.925 total time=   0.0s
[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.950 total time=   0.0s
[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.900 total time=   0.0s
[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.962 total time=   0.0s
[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.949 total time=   0.0s
[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.912 total time=   0.0s
[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.963 total time=   0.0s
[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.975 total time=   0.0s
[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.987 total time=   0.0s
[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.962 total time=   0.0s
[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.688 total time=   0.0s
[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.688 total time=   0.0s
[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.688 total time=   0.0s
[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.684 total time=   0.0s
[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.709 total time=   0.0s
[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.637 total time=   0.0s
[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.637 total time=   0.0s
[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s
[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.633 total time=   0.0s
[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.633 total time=   0.0s
[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.625 total time=   0.0s
[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s
[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s
[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.950 total time=   0.0s
[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.950 total time=   0.0s
[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.963 total time=   0.0s
[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.975 total time=   0.0s
[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.987 total time=   0.0s
[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.950 total time=   0.0s
[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.988 total time=   0.0s
[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.988 total time=   0.0s
[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.987 total time=   0.0s
[CV 5/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.975 total time=   0.0s
[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.912 total time=   0.0s
[CV 2/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.963 total time=   0.0s
[CV 3/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 4/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.987 total time=   0.0s
[CV 5/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.962 total time=   0.0s
[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.688 total time=   0.0s
[CV 2/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.700 total time=   0.0s
[CV 3/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.700 total time=   0.0s
[CV 4/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.696 total time=   0.0s
[CV 5/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.709 total time=   0.0s
[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 2/5] END .........C=10, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 3/5] END .........C=10, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 4/5] END .........C=10, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s
[CV 5/5] END .........C=10, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s
[CV 1/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.950 total time=   0.0s
[CV 2/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.963 total time=   0.0s
[CV 3/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.963 total time=   0.0s
[CV 4/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.987 total time=   0.0s
[CV 5/5] END .......C=10, gamma=0.1, kernel=rbf;, score=0.987 total time=   0.0s
[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.963 total time=   0.0s
[CV 2/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.975 total time=   0.0s
[CV 3/5] END ......C=10, gamma=0.01, kernel=rbf;, score=1.000 total time=   0.0s
[CV 4/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.975 total time=   0.0s
[CV 5/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.987 total time=   0.0s
[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.950 total time=   0.0s
[CV 2/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.988 total time=   0.0s
[CV 3/5] END .....C=10, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.0s
[CV 4/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.987 total time=   0.0s
[CV 5/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.912 total time=   0.0s
[CV 2/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.963 total time=   0.0s
[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.987 total time=   0.0s
[CV 5/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.949 total time=   0.0s
[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s
[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s
[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.950 total time=   0.0s
[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.963 total time=   0.0s
[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.975 total time=   0.0s
[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.975 total time=   0.0s
[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.987 total time=   0.0s
[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.975 total time=   0.0s
[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.938 total time=   0.0s
[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.975 total time=   0.0s
[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.949 total time=   0.0s
[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.975 total time=   0.0s
[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.950 total time=   0.0s
[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=1.000 total time=   0.0s
[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.987 total time=   0.0s
[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.950 total time=   0.0s
[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.988 total time=   0.0s
[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.0s
[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.987 total time=   0.0s
[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.637 total time=   0.0s
[CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s
[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.633 total time=   0.0s
[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.950 total time=   0.0s
[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.963 total time=   0.0s
[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.975 total time=   0.0s
[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.975 total time=   0.0s
[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.987 total time=   0.0s
[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.950 total time=   0.0s
[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.950 total time=   0.0s
[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.950 total time=   0.0s
[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.937 total time=   0.0s
[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.975 total time=   0.0s
[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.963 total time=   0.0s
[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.949 total time=   0.0s
[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.950 total time=   0.0s
[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=1.000 total time=   0.0s
[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.975 total time=   0.0s
[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.987 total time=   0.0s

GridSearchCV(estimator=SVC(),
             param_grid={'C': [0.1, 1, 10, 100, 1000],
                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
                         'kernel': ['rbf']},
             verbose=3)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

You can inspect the best parameters found by GridSearchCV using the bestparams attribute, and the best estimator using the bestestimator attribute. Here we see that the best set of parameters from the ones we specified are 10 for c value, 0.01 for gamma, and 'rbf' for the kernel.

grid.best_params_

{'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}

Then you can re-run predictions on this grid object just like you would with a normal model.

grid_predictions = grid.predict(X_test)

print(confusion_matrix(y_test,grid_predictions))
print(classification_report(y_test,grid_predictions))

[[105   0]
 [  2  64]]
              precision    recall  f1-score   support

           B       0.98      1.00      0.99       105
           M       1.00      0.97      0.98        66

    accuracy                           0.99       171
   macro avg       0.99      0.98      0.99       171
weighted avg       0.99      0.99      0.99       171

# Modified Work
grid2 = GridSearchCV(SVC(), {'C': [0.1, 0.5, 1, 10, 100, 1000], 'gamma': [1,0.1,0.01,0.001,0.0001], 'kernel': ['rbf', 'linear']}, refit=True, verbose=2)
grid2.fit(X2_train, y2_train)

Fitting 5 folds for each of 60 candidates, totalling 300 fits
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=0.1, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=0.1, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=0.1, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=0.1, gamma=1, kernel=linear; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=0.1, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=0.1, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.1, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END .................C=0.1, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=0.1, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=0.1, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=0.1, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=0.1, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .........................C=0.5, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.5, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.5, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.5, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=0.5, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.5, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=0.5, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=0.5, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=0.5, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=0.5, gamma=1, kernel=linear; total time=   0.0s
[CV] END .......................C=0.5, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.5, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.5, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.5, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=0.5, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.5, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=0.5, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=0.5, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=0.5, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=0.5, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ......................C=0.5, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.5, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.5, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.5, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=0.5, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ...................C=0.5, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=0.5, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=0.5, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=0.5, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=0.5, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END .....................C=0.5, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.5, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.5, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.5, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=0.5, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ..................C=0.5, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=0.5, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=0.5, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=0.5, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=0.5, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ....................C=0.5, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.5, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.5, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.5, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=0.5, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END .................C=0.5, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=0.5, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=0.5, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=0.5, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=0.5, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=1, kernel=linear; total time=   0.0s
[CV] END ........................C=1, gamma=1, kernel=linear; total time=   0.0s
[CV] END ........................C=1, gamma=1, kernel=linear; total time=   0.0s
[CV] END ........................C=1, gamma=1, kernel=linear; total time=   0.0s
[CV] END ........................C=1, gamma=1, kernel=linear; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=1, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ......................C=1, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ......................C=1, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ......................C=1, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ......................C=1, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=1, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END .....................C=1, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END .....................C=1, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END .....................C=1, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END .....................C=1, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .......................C=1, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=1, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ....................C=1, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ....................C=1, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ....................C=1, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ....................C=1, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ......................C=1, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ......................C=1, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ......................C=1, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ......................C=1, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ......................C=1, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ...................C=1, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ...................C=1, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ...................C=1, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ...................C=1, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ...................C=1, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=1, kernel=linear; total time=   0.0s
[CV] END .......................C=10, gamma=1, kernel=linear; total time=   0.0s
[CV] END .......................C=10, gamma=1, kernel=linear; total time=   0.0s
[CV] END .......................C=10, gamma=1, kernel=linear; total time=   0.0s
[CV] END .......................C=10, gamma=1, kernel=linear; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .....................C=10, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END .....................C=10, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END .....................C=10, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END .....................C=10, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END .....................C=10, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ....................C=10, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ....................C=10, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ....................C=10, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ....................C=10, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ....................C=10, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ......................C=10, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ...................C=10, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ...................C=10, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ...................C=10, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ...................C=10, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ...................C=10, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END .....................C=10, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END .....................C=10, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END .....................C=10, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END .....................C=10, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END .....................C=10, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ..................C=10, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ..................C=10, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ..................C=10, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ..................C=10, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ..................C=10, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=100, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=100, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=100, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=100, gamma=1, kernel=linear; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ....................C=100, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=100, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=100, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=100, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ....................C=100, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ...................C=100, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=100, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=100, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=100, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ...................C=100, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .....................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ..................C=100, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=100, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=100, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=100, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ..................C=100, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ....................C=100, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=100, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=100, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=100, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ....................C=100, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END .................C=100, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=100, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=100, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=100, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END .................C=100, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ........................C=1000, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ........................C=1000, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ........................C=1000, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ........................C=1000, gamma=1, kernel=rbf; total time=   0.0s
[CV] END ........................C=1000, gamma=1, kernel=rbf; total time=   0.0s
[CV] END .....................C=1000, gamma=1, kernel=linear; total time=   0.0s
[CV] END .....................C=1000, gamma=1, kernel=linear; total time=   0.0s
[CV] END .....................C=1000, gamma=1, kernel=linear; total time=   0.0s
[CV] END .....................C=1000, gamma=1, kernel=linear; total time=   0.0s
[CV] END .....................C=1000, gamma=1, kernel=linear; total time=   0.0s
[CV] END ......................C=1000, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=1000, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=1000, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=1000, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ......................C=1000, gamma=0.1, kernel=rbf; total time=   0.0s
[CV] END ...................C=1000, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ...................C=1000, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ...................C=1000, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ...................C=1000, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END ...................C=1000, gamma=0.1, kernel=linear; total time=   0.0s
[CV] END .....................C=1000, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=1000, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=1000, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=1000, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END .....................C=1000, gamma=0.01, kernel=rbf; total time=   0.0s
[CV] END ..................C=1000, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ..................C=1000, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ..................C=1000, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ..................C=1000, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ..................C=1000, gamma=0.01, kernel=linear; total time=   0.0s
[CV] END ....................C=1000, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=1000, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=1000, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=1000, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END ....................C=1000, gamma=0.001, kernel=rbf; total time=   0.0s
[CV] END .................C=1000, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END .................C=1000, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END .................C=1000, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END .................C=1000, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END .................C=1000, gamma=0.001, kernel=linear; total time=   0.0s
[CV] END ...................C=1000, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ...................C=1000, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ...................C=1000, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ...................C=1000, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ...................C=1000, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV] END ................C=1000, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ................C=1000, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ................C=1000, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ................C=1000, gamma=0.0001, kernel=linear; total time=   0.0s
[CV] END ................C=1000, gamma=0.0001, kernel=linear; total time=   0.0s

GridSearchCV(estimator=SVC(),
             param_grid={'C': [0.1, 0.5, 1, 10, 100, 1000],
                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
                         'kernel': ['rbf', 'linear']},
             verbose=2)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

grid2.best_params_

{'C': 0.1, 'gamma': 1, 'kernel': 'linear'}

grid2_predictions = grid2.predict(X2_test)
print(confusion_matrix(y2_test, grid2_predictions))
print(classification_report(y2_test, grid2_predictions))

[[72  0]
 [ 1 41]]
              precision    recall  f1-score   support

           B       0.99      1.00      0.99        72
           M       1.00      0.98      0.99        42

    accuracy                           0.99       114
   macro avg       0.99      0.99      0.99       114
weighted avg       0.99      0.99      0.99       114

Nice! We got a slightly better improvement using these parameters, though our original accuracy was already very good. Keep this grid search in mind when you need to do hyperparameter tuning. It can save you a lot of time.

Congrats! 🙂 You now know how to use SVM and hyperparameter tuning in sklearn. Try using this on your own dataset and refer back to this lecture if you get stuck.